CIIR Talk Series: Sean MacAvaney

Speaker: Sean MacAvaney, University of Glasgow

Title: Re-Thinking Re-Ranking

Date: Friday, October 20, 2023 - 1:30 - 2:30 PM EDT (North American Eastern Daylight Saving Time) via Zoom. On campus attendees will gather in CS 151 to view the presentation.

Abstract: Re-ranking systems take a "cascading" approach, wherein an initial candidate pool of documents are ranked and filtered to produce a final result list. This approach exhibits a fundamental relevance misalignment problem: the most relevant documents may be filtered out by a prior stage as insufficiently relevant, ultimately reducing recall and limiting the potential effectiveness. In this talk, I challenge the cascading paradigm by proposing methods that efficiently pull in additional potentially-relevant documents during the re-ranking process, using the long-standing Cluster Hypothesis. I demonstrate that these methods can improve the efficiency and effectiveness of both bi-encoder and cross-encoder retrieval models at various operational points. Cascading is dead, long live re-ranking!

Bio: Sean MacAvaney is a Lecturer in Machine Learning at the University of Glasgow and a member of the Terrier Team. His research primarily focuses on effective and efficient neural retrieval. He completed his PhD at Georgetown University in 2021, where he was a member of the IR Lab and an ARCS Endowed Scholar. He was a co-recipient of the SIGIR 2023 Best Paper Award and the ECIR 2023 Best Short Paper Award.

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